Overview

Brought to you by YData

Dataset statistics

Number of variables37
Number of observations25000
Missing cells3752
Missing cells (%)0.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory18.9 MiB
Average record size in memory792.1 B

Variable types

Numeric20
DateTime2
Categorical15

Alerts

attrited is highly overall correlated with exit_interview_scheduled and 1 other fieldsHigh correlation
base_salary is highly overall correlated with is_manager and 2 other fieldsHigh correlation
department is highly overall correlated with roleHigh correlation
exit_interview_scheduled is highly overall correlated with attrited and 1 other fieldsHigh correlation
is_manager is highly overall correlated with base_salary and 3 other fieldsHigh correlation
level is highly overall correlated with is_managerHigh correlation
months_since_hire is highly overall correlated with tenure_yearsHigh correlation
offboarding_ticket_created is highly overall correlated with attrited and 1 other fieldsHigh correlation
role is highly overall correlated with departmentHigh correlation
salary_band is highly overall correlated with base_salary and 1 other fieldsHigh correlation
stock_grants is highly overall correlated with base_salary and 1 other fieldsHigh correlation
tenure_years is highly overall correlated with months_since_hireHigh correlation
night_shift is highly imbalanced (52.3%) Imbalance
engagement_score has 1966 (7.9%) missing values Missing
manager_quality has 1786 (7.1%) missing values Missing
employee_id is uniformly distributed Uniform
salary_band is uniformly distributed Uniform
employee_id has unique values Unique
commute_km has 8772 (35.1%) zeros Zeros
internal_moves_last_2y has 19454 (77.8%) zeros Zeros
stock_grants has 17002 (68.0%) zeros Zeros
sick_days has 312 (1.2%) zeros Zeros

Reproduction

Analysis started2025-08-20 05:16:29.267545
Analysis finished2025-08-20 05:17:07.647208
Duration38.38 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

employee_id
Real number (ℝ)

Uniform  Unique 

Distinct25000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean112499.5
Minimum100000
Maximum124999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size195.4 KiB
2025-08-20T15:17:07.724713image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum100000
5-th percentile101249.95
Q1106249.75
median112499.5
Q3118749.25
95-th percentile123749.05
Maximum124999
Range24999
Interquartile range (IQR)12499.5

Descriptive statistics

Standard deviation7217.0227
Coefficient of variation (CV)0.064151598
Kurtosis-1.2
Mean112499.5
Median Absolute Deviation (MAD)6250
Skewness0
Sum2.8124875 × 109
Variance52085417
MonotonicityNot monotonic
2025-08-20T15:17:07.850131image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
121752 1
 
< 0.1%
102685 1
 
< 0.1%
100422 1
 
< 0.1%
102439 1
 
< 0.1%
105626 1
 
< 0.1%
115177 1
 
< 0.1%
115842 1
 
< 0.1%
119502 1
 
< 0.1%
111539 1
 
< 0.1%
101860 1
 
< 0.1%
Other values (24990) 24990
> 99.9%
ValueCountFrequency (%)
100000 1
< 0.1%
100001 1
< 0.1%
100002 1
< 0.1%
100003 1
< 0.1%
100004 1
< 0.1%
100005 1
< 0.1%
100006 1
< 0.1%
100007 1
< 0.1%
100008 1
< 0.1%
100009 1
< 0.1%
ValueCountFrequency (%)
124999 1
< 0.1%
124998 1
< 0.1%
124997 1
< 0.1%
124996 1
< 0.1%
124995 1
< 0.1%
124994 1
< 0.1%
124993 1
< 0.1%
124992 1
< 0.1%
124991 1
< 0.1%
124990 1
< 0.1%
Distinct1826
Distinct (%)7.3%
Missing0
Missing (%)0.0%
Memory size195.4 KiB
Minimum2020-01-01 00:00:00
Maximum2024-12-30 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-08-20T15:17:07.962231image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-20T15:17:08.080916image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct3646
Distinct (%)14.6%
Missing0
Missing (%)0.0%
Memory size195.4 KiB
Minimum2010-01-03 00:00:00
Maximum2020-01-01 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-08-20T15:17:08.198527image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-20T15:17:08.311417image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

region
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
AMER
10032 
EMEA
8555 
APAC
6413 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters100000
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAMER
2nd rowAPAC
3rd rowEMEA
4th rowAMER
5th rowAPAC

Common Values

ValueCountFrequency (%)
AMER 10032
40.1%
EMEA 8555
34.2%
APAC 6413
25.7%

Length

2025-08-20T15:17:08.417543image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-20T15:17:08.583082image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
amer 10032
40.1%
emea 8555
34.2%
apac 6413
25.7%

Most occurring characters

ValueCountFrequency (%)
A 31413
31.4%
E 27142
27.1%
M 18587
18.6%
R 10032
 
10.0%
P 6413
 
6.4%
C 6413
 
6.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 100000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 31413
31.4%
E 27142
27.1%
M 18587
18.6%
R 10032
 
10.0%
P 6413
 
6.4%
C 6413
 
6.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 100000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 31413
31.4%
E 27142
27.1%
M 18587
18.6%
R 10032
 
10.0%
P 6413
 
6.4%
C 6413
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 100000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 31413
31.4%
E 27142
27.1%
M 18587
18.6%
R 10032
 
10.0%
P 6413
 
6.4%
C 6413
 
6.4%

department
Categorical

High correlation 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
Engineering
7060 
Sales
4435 
Customer Success
3483 
Finance
2522 
Marketing
2505 
Other values (3)
4995 

Length

Max length16
Median length10
Mean length9.19164
Min length2

Characters and Unicode

Total characters229791
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSales
2nd rowEngineering
3rd rowSales
4th rowEngineering
5th rowHR

Common Values

ValueCountFrequency (%)
Engineering 7060
28.2%
Sales 4435
17.7%
Customer Success 3483
13.9%
Finance 2522
 
10.1%
Marketing 2505
 
10.0%
Operations 2063
 
8.3%
Product 1507
 
6.0%
HR 1425
 
5.7%

Length

2025-08-20T15:17:08.680240image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-20T15:17:08.784600image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
engineering 7060
24.8%
sales 4435
15.6%
customer 3483
12.2%
success 3483
12.2%
finance 2522
 
8.9%
marketing 2505
 
8.8%
operations 2063
 
7.2%
product 1507
 
5.3%
hr 1425
 
5.0%

Most occurring characters

ValueCountFrequency (%)
e 32611
14.2%
n 30792
13.4%
i 21210
 
9.2%
s 16947
 
7.4%
g 16625
 
7.2%
r 16618
 
7.2%
a 11525
 
5.0%
c 10995
 
4.8%
t 9558
 
4.2%
u 8473
 
3.7%
Other values (16) 54437
23.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 229791
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 32611
14.2%
n 30792
13.4%
i 21210
 
9.2%
s 16947
 
7.4%
g 16625
 
7.2%
r 16618
 
7.2%
a 11525
 
5.0%
c 10995
 
4.8%
t 9558
 
4.2%
u 8473
 
3.7%
Other values (16) 54437
23.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 229791
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 32611
14.2%
n 30792
13.4%
i 21210
 
9.2%
s 16947
 
7.4%
g 16625
 
7.2%
r 16618
 
7.2%
a 11525
 
5.0%
c 10995
 
4.8%
t 9558
 
4.2%
u 8473
 
3.7%
Other values (16) 54437
23.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 229791
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 32611
14.2%
n 30792
13.4%
i 21210
 
9.2%
s 16947
 
7.4%
g 16625
 
7.2%
r 16618
 
7.2%
a 11525
 
5.0%
c 10995
 
4.8%
t 9558
 
4.2%
u 8473
 
3.7%
Other values (16) 54437
23.7%

role
Categorical

High correlation 

Distinct34
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
SWE II
 
1231
SWE I
 
1213
Data Engineer
 
1189
Support Rep
 
1180
CSM
 
1169
Other values (29)
19018 

Length

Max length15
Median length11
Mean length8.65908
Min length2

Characters and Unicode

Total characters216477
Distinct characters38
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSales Manager
2nd rowSWE II
3rd rowAE
4th rowML Engineer
5th rowHRBP

Common Values

ValueCountFrequency (%)
SWE II 1231
 
4.9%
SWE I 1213
 
4.9%
Data Engineer 1189
 
4.8%
Support Rep 1180
 
4.7%
CSM 1169
 
4.7%
Eng Manager 1154
 
4.6%
Senior SWE 1142
 
4.6%
Support Manager 1134
 
4.5%
Sales Manager 1131
 
4.5%
ML Engineer 1131
 
4.5%
Other values (24) 13326
53.3%

Length

2025-08-20T15:17:08.907677image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
manager 4758
 
11.4%
ops 3930
 
9.4%
swe 3586
 
8.6%
engineer 2320
 
5.5%
support 2314
 
5.5%
sales 2231
 
5.3%
analyst 1270
 
3.0%
ii 1231
 
2.9%
i 1213
 
2.9%
data 1189
 
2.8%
Other values (28) 17818
42.6%

Most occurring characters

ValueCountFrequency (%)
e 19835
 
9.2%
a 19376
 
9.0%
n 17512
 
8.1%
16860
 
7.8%
r 14255
 
6.6%
S 12007
 
5.5%
p 10103
 
4.7%
g 9003
 
4.2%
M 8991
 
4.2%
E 8591
 
4.0%
Other values (28) 79944
36.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 216477
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 19835
 
9.2%
a 19376
 
9.0%
n 17512
 
8.1%
16860
 
7.8%
r 14255
 
6.6%
S 12007
 
5.5%
p 10103
 
4.7%
g 9003
 
4.2%
M 8991
 
4.2%
E 8591
 
4.0%
Other values (28) 79944
36.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 216477
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 19835
 
9.2%
a 19376
 
9.0%
n 17512
 
8.1%
16860
 
7.8%
r 14255
 
6.6%
S 12007
 
5.5%
p 10103
 
4.7%
g 9003
 
4.2%
M 8991
 
4.2%
E 8591
 
4.0%
Other values (28) 79944
36.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 216477
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 19835
 
9.2%
a 19376
 
9.0%
n 17512
 
8.1%
16860
 
7.8%
r 14255
 
6.6%
S 12007
 
5.5%
p 10103
 
4.7%
g 9003
 
4.2%
M 8991
 
4.2%
E 8591
 
4.0%
Other values (28) 79944
36.9%

level
Categorical

High correlation 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
IC2
6769 
IC3
5663 
IC1
4570 
Manager
3015 
IC4
3001 
Other values (2)
1982 

Length

Max length8
Median length3
Mean length3.75424
Min length2

Characters and Unicode

Total characters93856
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIC3
2nd rowManager
3rd rowIC2
4th rowIC4
5th rowIC3

Common Values

ValueCountFrequency (%)
IC2 6769
27.1%
IC3 5663
22.7%
IC1 4570
18.3%
Manager 3015
12.1%
IC4 3001
12.0%
Director 1463
 
5.9%
VP 519
 
2.1%

Length

2025-08-20T15:17:09.013278image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-20T15:17:09.107078image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
ic2 6769
27.1%
ic3 5663
22.7%
ic1 4570
18.3%
manager 3015
12.1%
ic4 3001
12.0%
director 1463
 
5.9%
vp 519
 
2.1%

Most occurring characters

ValueCountFrequency (%)
I 20003
21.3%
C 20003
21.3%
2 6769
 
7.2%
a 6030
 
6.4%
r 5941
 
6.3%
3 5663
 
6.0%
1 4570
 
4.9%
e 4478
 
4.8%
g 3015
 
3.2%
n 3015
 
3.2%
Other values (9) 14369
15.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 93856
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
I 20003
21.3%
C 20003
21.3%
2 6769
 
7.2%
a 6030
 
6.4%
r 5941
 
6.3%
3 5663
 
6.0%
1 4570
 
4.9%
e 4478
 
4.8%
g 3015
 
3.2%
n 3015
 
3.2%
Other values (9) 14369
15.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 93856
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
I 20003
21.3%
C 20003
21.3%
2 6769
 
7.2%
a 6030
 
6.4%
r 5941
 
6.3%
3 5663
 
6.0%
1 4570
 
4.9%
e 4478
 
4.8%
g 3015
 
3.2%
n 3015
 
3.2%
Other values (9) 14369
15.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 93856
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
I 20003
21.3%
C 20003
21.3%
2 6769
 
7.2%
a 6030
 
6.4%
r 5941
 
6.3%
3 5663
 
6.0%
1 4570
 
4.9%
e 4478
 
4.8%
g 3015
 
3.2%
n 3015
 
3.2%
Other values (9) 14369
15.3%

is_manager
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
20003 
1
4997 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters25000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 20003
80.0%
1 4997
 
20.0%

Length

2025-08-20T15:17:09.209816image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-20T15:17:09.287324image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0 20003
80.0%
1 4997
 
20.0%

Most occurring characters

ValueCountFrequency (%)
0 20003
80.0%
1 4997
 
20.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 25000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 20003
80.0%
1 4997
 
20.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 25000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 20003
80.0%
1 4997
 
20.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 25000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 20003
80.0%
1 4997
 
20.0%

age
Real number (ℝ)

Distinct47
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.99396
Minimum18
Maximum64
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size195.4 KiB
2025-08-20T15:17:09.380411image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile22
Q129
median34
Q339
95-th percentile46
Maximum64
Range46
Interquartile range (IQR)10

Descriptive statistics

Standard deviation7.3444619
Coefficient of variation (CV)0.21605197
Kurtosis-0.20767657
Mean33.99396
Median Absolute Deviation (MAD)5
Skewness0.088059903
Sum849849
Variance53.941121
MonotonicityNot monotonic
2025-08-20T15:17:09.499324image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
34 1361
 
5.4%
35 1348
 
5.4%
32 1319
 
5.3%
36 1313
 
5.3%
33 1312
 
5.2%
31 1252
 
5.0%
37 1200
 
4.8%
30 1140
 
4.6%
29 1121
 
4.5%
38 1081
 
4.3%
Other values (37) 12553
50.2%
ValueCountFrequency (%)
18 493
2.0%
19 170
 
0.7%
20 246
 
1.0%
21 295
 
1.2%
22 369
1.5%
23 434
1.7%
24 527
2.1%
25 639
2.6%
26 788
3.2%
27 819
3.3%
ValueCountFrequency (%)
64 1
 
< 0.1%
63 1
 
< 0.1%
62 1
 
< 0.1%
61 4
 
< 0.1%
60 1
 
< 0.1%
59 3
 
< 0.1%
58 9
< 0.1%
57 9
< 0.1%
56 16
0.1%
55 22
0.1%

gender
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
M
13017 
F
11292 
Other/Undisclosed
 
691

Length

Max length17
Median length1
Mean length1.44224
Min length1

Characters and Unicode

Total characters36056
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowF
2nd rowF
3rd rowM
4th rowM
5th rowF

Common Values

ValueCountFrequency (%)
M 13017
52.1%
F 11292
45.2%
Other/Undisclosed 691
 
2.8%

Length

2025-08-20T15:17:09.611854image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-20T15:17:09.695493image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
m 13017
52.1%
f 11292
45.2%
other/undisclosed 691
 
2.8%

Most occurring characters

ValueCountFrequency (%)
M 13017
36.1%
F 11292
31.3%
e 1382
 
3.8%
d 1382
 
3.8%
s 1382
 
3.8%
O 691
 
1.9%
t 691
 
1.9%
h 691
 
1.9%
r 691
 
1.9%
/ 691
 
1.9%
Other values (6) 4146
 
11.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 36056
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
M 13017
36.1%
F 11292
31.3%
e 1382
 
3.8%
d 1382
 
3.8%
s 1382
 
3.8%
O 691
 
1.9%
t 691
 
1.9%
h 691
 
1.9%
r 691
 
1.9%
/ 691
 
1.9%
Other values (6) 4146
 
11.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 36056
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
M 13017
36.1%
F 11292
31.3%
e 1382
 
3.8%
d 1382
 
3.8%
s 1382
 
3.8%
O 691
 
1.9%
t 691
 
1.9%
h 691
 
1.9%
r 691
 
1.9%
/ 691
 
1.9%
Other values (6) 4146
 
11.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 36056
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
M 13017
36.1%
F 11292
31.3%
e 1382
 
3.8%
d 1382
 
3.8%
s 1382
 
3.8%
O 691
 
1.9%
t 691
 
1.9%
h 691
 
1.9%
r 691
 
1.9%
/ 691
 
1.9%
Other values (6) 4146
 
11.5%

remote_status
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
Hybrid
11211 
Remote
8769 
Onsite
5020 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters150000
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHybrid
2nd rowHybrid
3rd rowOnsite
4th rowRemote
5th rowOnsite

Common Values

ValueCountFrequency (%)
Hybrid 11211
44.8%
Remote 8769
35.1%
Onsite 5020
20.1%

Length

2025-08-20T15:17:09.783425image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-20T15:17:09.862422image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
hybrid 11211
44.8%
remote 8769
35.1%
onsite 5020
20.1%

Most occurring characters

ValueCountFrequency (%)
e 22558
15.0%
i 16231
10.8%
t 13789
9.2%
H 11211
7.5%
y 11211
7.5%
b 11211
7.5%
r 11211
7.5%
d 11211
7.5%
R 8769
 
5.8%
m 8769
 
5.8%
Other values (4) 23829
15.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 150000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 22558
15.0%
i 16231
10.8%
t 13789
9.2%
H 11211
7.5%
y 11211
7.5%
b 11211
7.5%
r 11211
7.5%
d 11211
7.5%
R 8769
 
5.8%
m 8769
 
5.8%
Other values (4) 23829
15.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 150000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 22558
15.0%
i 16231
10.8%
t 13789
9.2%
H 11211
7.5%
y 11211
7.5%
b 11211
7.5%
r 11211
7.5%
d 11211
7.5%
R 8769
 
5.8%
m 8769
 
5.8%
Other values (4) 23829
15.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 150000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 22558
15.0%
i 16231
10.8%
t 13789
9.2%
H 11211
7.5%
y 11211
7.5%
b 11211
7.5%
r 11211
7.5%
d 11211
7.5%
R 8769
 
5.8%
m 8769
 
5.8%
Other values (4) 23829
15.9%

commute_km
Real number (ℝ)

Zeros 

Distinct496
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.752276
Minimum0
Maximum72.8
Zeros8772
Zeros (%)35.1%
Negative0
Negative (%)0.0%
Memory size195.4 KiB
2025-08-20T15:17:09.963230image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median5.5
Q312.4
95-th percentile25
Maximum72.8
Range72.8
Interquartile range (IQR)12.4

Descriptive statistics

Standard deviation8.8430181
Coefficient of variation (CV)1.1406996
Kurtosis2.7375242
Mean7.752276
Median Absolute Deviation (MAD)5.5
Skewness1.4610987
Sum193806.9
Variance78.198968
MonotonicityNot monotonic
2025-08-20T15:17:10.080078image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 8772
35.1%
5.3 119
 
0.5%
8.1 116
 
0.5%
7.2 115
 
0.5%
8.8 115
 
0.5%
8.3 114
 
0.5%
4.9 112
 
0.4%
5 112
 
0.4%
5.6 111
 
0.4%
4.4 110
 
0.4%
Other values (486) 15204
60.8%
ValueCountFrequency (%)
0 8772
35.1%
0.1 1
 
< 0.1%
0.2 10
 
< 0.1%
0.3 13
 
0.1%
0.4 12
 
< 0.1%
0.5 25
 
0.1%
0.6 35
 
0.1%
0.7 24
 
0.1%
0.8 21
 
0.1%
0.9 32
 
0.1%
ValueCountFrequency (%)
72.8 1
< 0.1%
72.6 1
< 0.1%
69.2 1
< 0.1%
65.6 1
< 0.1%
65.4 1
< 0.1%
64.9 1
< 0.1%
61 1
< 0.1%
60.9 1
< 0.1%
60.3 1
< 0.1%
58.5 1
< 0.1%

tenure_years
Real number (ℝ)

High correlation 

Distinct1460
Distinct (%)5.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.4997008
Minimum0.06
Maximum14.94
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size195.4 KiB
2025-08-20T15:17:10.199438image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0.06
5-th percentile2.24
Q14.99
median7.53
Q310
95-th percentile12.69
Maximum14.94
Range14.88
Interquartile range (IQR)5.01

Descriptive statistics

Standard deviation3.2250834
Coefficient of variation (CV)0.43002827
Kurtosis-0.82547659
Mean7.4997008
Median Absolute Deviation (MAD)2.505
Skewness-0.010356751
Sum187492.52
Variance10.401163
MonotonicityNot monotonic
2025-08-20T15:17:10.319458image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.84 43
 
0.2%
7.16 42
 
0.2%
5.28 40
 
0.2%
9.14 40
 
0.2%
9.6 40
 
0.2%
5.75 39
 
0.2%
9.34 39
 
0.2%
6.87 38
 
0.2%
5.97 38
 
0.2%
9.28 38
 
0.2%
Other values (1450) 24603
98.4%
ValueCountFrequency (%)
0.06 1
< 0.1%
0.09 1
< 0.1%
0.1 2
< 0.1%
0.12 1
< 0.1%
0.13 1
< 0.1%
0.14 1
< 0.1%
0.15 1
< 0.1%
0.16 2
< 0.1%
0.19 1
< 0.1%
0.21 2
< 0.1%
ValueCountFrequency (%)
14.94 2
< 0.1%
14.92 1
< 0.1%
14.89 1
< 0.1%
14.88 1
< 0.1%
14.86 1
< 0.1%
14.83 2
< 0.1%
14.8 1
< 0.1%
14.79 2
< 0.1%
14.78 1
< 0.1%
14.77 2
< 0.1%

base_salary
Real number (ℝ)

High correlation 

Distinct22455
Distinct (%)89.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean116362.83
Minimum45000
Maximum348743
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size195.4 KiB
2025-08-20T15:17:10.436241image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum45000
5-th percentile68636.95
Q189044.5
median109374
Q3135678.5
95-th percentile187896
Maximum348743
Range303743
Interquartile range (IQR)46634

Descriptive statistics

Standard deviation38150.254
Coefficient of variation (CV)0.327856
Kurtosis2.6645665
Mean116362.83
Median Absolute Deviation (MAD)22589.5
Skewness1.2811982
Sum2.9090709 × 109
Variance1.4554419 × 109
MonotonicityNot monotonic
2025-08-20T15:17:10.548661image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
78168 5
 
< 0.1%
100759 4
 
< 0.1%
91990 4
 
< 0.1%
95372 4
 
< 0.1%
96988 4
 
< 0.1%
115119 4
 
< 0.1%
91745 4
 
< 0.1%
113201 4
 
< 0.1%
87411 4
 
< 0.1%
103192 4
 
< 0.1%
Other values (22445) 24959
99.8%
ValueCountFrequency (%)
45000 2
< 0.1%
45953 1
< 0.1%
46879 1
< 0.1%
47482 1
< 0.1%
47483 1
< 0.1%
47499 1
< 0.1%
47599 1
< 0.1%
47797 1
< 0.1%
48413 1
< 0.1%
48837 1
< 0.1%
ValueCountFrequency (%)
348743 1
< 0.1%
333270 1
< 0.1%
332585 1
< 0.1%
328636 1
< 0.1%
328595 1
< 0.1%
327821 1
< 0.1%
327736 1
< 0.1%
327044 1
< 0.1%
325557 1
< 0.1%
324355 1
< 0.1%

compa_ratio
Real number (ℝ)

Distinct91
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0004432
Minimum0.56
Maximum1.48
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size195.4 KiB
2025-08-20T15:17:10.667035image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0.56
5-th percentile0.8
Q10.92
median1
Q31.08
95-th percentile1.2
Maximum1.48
Range0.92
Interquartile range (IQR)0.16

Descriptive statistics

Standard deviation0.11976685
Coefficient of variation (CV)0.11971379
Kurtosis-0.077725934
Mean1.0004432
Median Absolute Deviation (MAD)0.08
Skewness0.019523293
Sum25011.08
Variance0.014344097
MonotonicityNot monotonic
2025-08-20T15:17:10.786498image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.03 842
 
3.4%
0.99 837
 
3.3%
0.96 831
 
3.3%
0.97 824
 
3.3%
1.02 821
 
3.3%
1.04 815
 
3.3%
1.05 787
 
3.1%
0.98 784
 
3.1%
1.01 780
 
3.1%
1 773
 
3.1%
Other values (81) 16906
67.6%
ValueCountFrequency (%)
0.56 1
 
< 0.1%
0.57 1
 
< 0.1%
0.59 1
 
< 0.1%
0.6 1
 
< 0.1%
0.61 4
 
< 0.1%
0.62 7
< 0.1%
0.63 7
< 0.1%
0.64 10
< 0.1%
0.65 7
< 0.1%
0.66 9
< 0.1%
ValueCountFrequency (%)
1.48 1
 
< 0.1%
1.46 1
 
< 0.1%
1.45 1
 
< 0.1%
1.44 1
 
< 0.1%
1.43 2
 
< 0.1%
1.42 1
 
< 0.1%
1.41 2
 
< 0.1%
1.4 3
< 0.1%
1.39 6
< 0.1%
1.38 5
< 0.1%

avg_raise_3y
Real number (ℝ)

Distinct47
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0299848
Minimum0.008
Maximum0.056
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size195.4 KiB
2025-08-20T15:17:10.983357image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0.008
5-th percentile0.02
Q10.026
median0.03
Q30.034
95-th percentile0.04
Maximum0.056
Range0.048
Interquartile range (IQR)0.008

Descriptive statistics

Standard deviation0.0058103769
Coefficient of variation (CV)0.19377741
Kurtosis-0.022564766
Mean0.0299848
Median Absolute Deviation (MAD)0.004
Skewness0.015641571
Sum749.62
Variance3.3760479 × 10-5
MonotonicityNot monotonic
2025-08-20T15:17:11.101780image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
0.031 1736
 
6.9%
0.029 1728
 
6.9%
0.03 1701
 
6.8%
0.028 1625
 
6.5%
0.032 1589
 
6.4%
0.027 1562
 
6.2%
0.033 1497
 
6.0%
0.026 1314
 
5.3%
0.034 1302
 
5.2%
0.025 1183
 
4.7%
Other values (37) 9763
39.1%
ValueCountFrequency (%)
0.008 2
 
< 0.1%
0.01 4
 
< 0.1%
0.011 10
 
< 0.1%
0.012 10
 
< 0.1%
0.013 17
 
0.1%
0.014 32
 
0.1%
0.015 77
 
0.3%
0.016 87
0.3%
0.017 158
0.6%
0.018 201
0.8%
ValueCountFrequency (%)
0.056 1
 
< 0.1%
0.054 1
 
< 0.1%
0.053 1
 
< 0.1%
0.052 4
 
< 0.1%
0.051 2
 
< 0.1%
0.05 4
 
< 0.1%
0.049 4
 
< 0.1%
0.048 13
 
0.1%
0.047 22
0.1%
0.046 42
0.2%

time_since_last_promo_yrs
Real number (ℝ)

Distinct752
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9938844
Minimum0.03
Maximum10.89
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size195.4 KiB
2025-08-20T15:17:11.218249image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0.03
5-th percentile0.45
Q11.06
median1.74
Q32.65
95-th percentile4.3905
Maximum10.89
Range10.86
Interquartile range (IQR)1.59

Descriptive statistics

Standard deviation1.2619242
Coefficient of variation (CV)0.63289739
Kurtosis2.4135821
Mean1.9938844
Median Absolute Deviation (MAD)0.76
Skewness1.2621984
Sum49847.11
Variance1.5924528
MonotonicityNot monotonic
2025-08-20T15:17:11.335331image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.13 114
 
0.5%
1.4 113
 
0.5%
1.17 111
 
0.4%
1.18 108
 
0.4%
1.63 108
 
0.4%
1.15 107
 
0.4%
1.06 106
 
0.4%
1.48 106
 
0.4%
1.12 105
 
0.4%
1.46 104
 
0.4%
Other values (742) 23918
95.7%
ValueCountFrequency (%)
0.03 2
 
< 0.1%
0.04 2
 
< 0.1%
0.05 2
 
< 0.1%
0.06 3
 
< 0.1%
0.07 5
 
< 0.1%
0.08 6
 
< 0.1%
0.09 9
< 0.1%
0.1 6
 
< 0.1%
0.11 6
 
< 0.1%
0.12 15
0.1%
ValueCountFrequency (%)
10.89 1
< 0.1%
10.61 1
< 0.1%
10.6 1
< 0.1%
10.56 1
< 0.1%
9.92 1
< 0.1%
9.57 2
< 0.1%
9.18 1
< 0.1%
9.15 1
< 0.1%
9.12 1
< 0.1%
8.95 2
< 0.1%

internal_moves_last_2y
Real number (ℝ)

Zeros 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.24852
Minimum0
Maximum5
Zeros19454
Zeros (%)77.8%
Negative0
Negative (%)0.0%
Memory size195.4 KiB
2025-08-20T15:17:11.427522image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.49522482
Coefficient of variation (CV)1.992696
Kurtosis4.2156319
Mean0.24852
Median Absolute Deviation (MAD)0
Skewness2.0014418
Sum6213
Variance0.24524762
MonotonicityNot monotonic
2025-08-20T15:17:11.512588image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 19454
77.8%
1 4936
 
19.7%
2 559
 
2.2%
3 46
 
0.2%
4 4
 
< 0.1%
5 1
 
< 0.1%
ValueCountFrequency (%)
0 19454
77.8%
1 4936
 
19.7%
2 559
 
2.2%
3 46
 
0.2%
4 4
 
< 0.1%
5 1
 
< 0.1%
ValueCountFrequency (%)
5 1
 
< 0.1%
4 4
 
< 0.1%
3 46
 
0.2%
2 559
 
2.2%
1 4936
 
19.7%
0 19454
77.8%
Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
3
11354 
4
6181 
2
3738 
5
2486 
1
1241 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters25000
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row3
3rd row4
4th row5
5th row3

Common Values

ValueCountFrequency (%)
3 11354
45.4%
4 6181
24.7%
2 3738
 
15.0%
5 2486
 
9.9%
1 1241
 
5.0%

Length

2025-08-20T15:17:11.604688image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-20T15:17:11.689699image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
3 11354
45.4%
4 6181
24.7%
2 3738
 
15.0%
5 2486
 
9.9%
1 1241
 
5.0%

Most occurring characters

ValueCountFrequency (%)
3 11354
45.4%
4 6181
24.7%
2 3738
 
15.0%
5 2486
 
9.9%
1 1241
 
5.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 25000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 11354
45.4%
4 6181
24.7%
2 3738
 
15.0%
5 2486
 
9.9%
1 1241
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 25000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 11354
45.4%
4 6181
24.7%
2 3738
 
15.0%
5 2486
 
9.9%
1 1241
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 25000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 11354
45.4%
4 6181
24.7%
2 3738
 
15.0%
5 2486
 
9.9%
1 1241
 
5.0%

engagement_score
Real number (ℝ)

Missing 

Distinct710
Distinct (%)3.1%
Missing1966
Missing (%)7.9%
Infinite0
Infinite (%)0.0%
Mean7.2637258
Minimum2.03
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size195.4 KiB
2025-08-20T15:17:11.796662image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum2.03
5-th percentile5
Q16.36
median7.29
Q38.2
95-th percentile9.53
Maximum10
Range7.97
Interquartile range (IQR)1.84

Descriptive statistics

Standard deviation1.3544697
Coefficient of variation (CV)0.18647038
Kurtosis-0.21004836
Mean7.2637258
Median Absolute Deviation (MAD)0.92
Skewness-0.16110524
Sum167312.66
Variance1.8345883
MonotonicityNot monotonic
2025-08-20T15:17:11.921219image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 586
 
2.3%
7.38 85
 
0.3%
7.04 81
 
0.3%
7 81
 
0.3%
7.45 80
 
0.3%
7.18 80
 
0.3%
7.16 80
 
0.3%
7.15 79
 
0.3%
7.99 79
 
0.3%
7.32 79
 
0.3%
Other values (700) 21724
86.9%
(Missing) 1966
 
7.9%
ValueCountFrequency (%)
2.03 1
< 0.1%
2.08 1
< 0.1%
2.21 1
< 0.1%
2.41 1
< 0.1%
2.44 1
< 0.1%
2.48 1
< 0.1%
2.62 2
< 0.1%
2.63 1
< 0.1%
2.67 1
< 0.1%
2.7 1
< 0.1%
ValueCountFrequency (%)
10 586
2.3%
9.99 12
 
< 0.1%
9.98 7
 
< 0.1%
9.97 11
 
< 0.1%
9.96 7
 
< 0.1%
9.95 15
 
0.1%
9.94 4
 
< 0.1%
9.93 9
 
< 0.1%
9.92 7
 
< 0.1%
9.91 9
 
< 0.1%

manager_quality
Real number (ℝ)

Missing 

Distinct771
Distinct (%)3.3%
Missing1786
Missing (%)7.1%
Infinite0
Infinite (%)0.0%
Mean7.066444
Minimum1.01
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size195.4 KiB
2025-08-20T15:17:12.046010image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1.01
5-th percentile4.45
Q16.01
median7.09
Q38.16
95-th percentile9.72
Maximum10
Range8.99
Interquartile range (IQR)2.15

Descriptive statistics

Standard deviation1.5481619
Coefficient of variation (CV)0.21908641
Kurtosis-0.31130817
Mean7.066444
Median Absolute Deviation (MAD)1.08
Skewness-0.17360933
Sum164040.43
Variance2.3968051
MonotonicityNot monotonic
2025-08-20T15:17:12.160144image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 793
 
3.2%
6.84 74
 
0.3%
7.46 73
 
0.3%
6.75 73
 
0.3%
7.15 71
 
0.3%
6.76 70
 
0.3%
7.34 70
 
0.3%
7.09 69
 
0.3%
7.33 68
 
0.3%
7.11 68
 
0.3%
Other values (761) 21785
87.1%
(Missing) 1786
 
7.1%
ValueCountFrequency (%)
1.01 1
< 0.1%
1.39 1
< 0.1%
1.44 1
< 0.1%
1.54 1
< 0.1%
1.62 1
< 0.1%
1.64 1
< 0.1%
1.67 1
< 0.1%
1.71 1
< 0.1%
1.83 1
< 0.1%
1.94 1
< 0.1%
ValueCountFrequency (%)
10 793
3.2%
9.99 12
 
< 0.1%
9.98 8
 
< 0.1%
9.97 12
 
< 0.1%
9.96 7
 
< 0.1%
9.95 8
 
< 0.1%
9.94 17
 
0.1%
9.93 11
 
< 0.1%
9.92 10
 
< 0.1%
9.91 15
 
0.1%

workload_score
Real number (ℝ)

Distinct894
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.9869036
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size195.4 KiB
2025-08-20T15:17:12.279530image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.93
Q14.73
median6
Q37.26
95-th percentile9.02
Maximum10
Range9
Interquartile range (IQR)2.53

Descriptive statistics

Standard deviation1.8203789
Coefficient of variation (CV)0.30406017
Kurtosis-0.33749791
Mean5.9869036
Median Absolute Deviation (MAD)1.27
Skewness-0.065035976
Sum149672.59
Variance3.3137794
MonotonicityNot monotonic
2025-08-20T15:17:12.397202image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 315
 
1.3%
6.63 71
 
0.3%
1 70
 
0.3%
6.64 67
 
0.3%
6.01 66
 
0.3%
5.79 65
 
0.3%
5.59 65
 
0.3%
6.29 65
 
0.3%
4.88 64
 
0.3%
5.53 63
 
0.3%
Other values (884) 24089
96.4%
ValueCountFrequency (%)
1 70
0.3%
1.01 1
 
< 0.1%
1.04 4
 
< 0.1%
1.05 2
 
< 0.1%
1.06 2
 
< 0.1%
1.08 3
 
< 0.1%
1.09 2
 
< 0.1%
1.1 2
 
< 0.1%
1.11 2
 
< 0.1%
1.12 2
 
< 0.1%
ValueCountFrequency (%)
10 315
1.3%
9.99 6
 
< 0.1%
9.98 6
 
< 0.1%
9.97 8
 
< 0.1%
9.96 8
 
< 0.1%
9.95 4
 
< 0.1%
9.94 6
 
< 0.1%
9.93 6
 
< 0.1%
9.92 3
 
< 0.1%
9.91 6
 
< 0.1%

learning_hours_last_yr
Real number (ℝ)

Distinct811
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.968396
Minimum0.1
Maximum120.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size195.4 KiB
2025-08-20T15:17:12.517616image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile3.6
Q19.6
median16.8
Q326.8
95-th percentile47.5
Maximum120.3
Range120.2
Interquartile range (IQR)17.2

Descriptive statistics

Standard deviation14.10974
Coefficient of variation (CV)0.70660356
Kurtosis2.8394969
Mean19.968396
Median Absolute Deviation (MAD)8.2
Skewness1.4002644
Sum499209.9
Variance199.08476
MonotonicityNot monotonic
2025-08-20T15:17:12.639406image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.5 109
 
0.4%
8.3 108
 
0.4%
8.7 105
 
0.4%
9.4 104
 
0.4%
9 103
 
0.4%
14.1 102
 
0.4%
9.9 101
 
0.4%
10.6 100
 
0.4%
7.4 100
 
0.4%
7.1 99
 
0.4%
Other values (801) 23969
95.9%
ValueCountFrequency (%)
0.1 2
 
< 0.1%
0.2 4
 
< 0.1%
0.3 7
 
< 0.1%
0.4 5
 
< 0.1%
0.5 13
0.1%
0.6 14
0.1%
0.7 12
< 0.1%
0.8 19
0.1%
0.9 15
0.1%
1 15
0.1%
ValueCountFrequency (%)
120.3 1
< 0.1%
119.5 1
< 0.1%
114.5 1
< 0.1%
112.8 1
< 0.1%
112.7 1
< 0.1%
106.7 1
< 0.1%
105.9 1
< 0.1%
105.7 1
< 0.1%
105.1 1
< 0.1%
103.7 1
< 0.1%

overtime_hours_month
Real number (ℝ)

Distinct548
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.130552
Minimum0.1
Maximum89.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size195.4 KiB
2025-08-20T15:17:12.756079image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile2.6
Q16.6
median11.1
Q317.6
95-th percentile30.4
Maximum89.9
Range89.8
Interquartile range (IQR)11

Descriptive statistics

Standard deviation8.9076836
Coefficient of variation (CV)0.67839369
Kurtosis3.0310011
Mean13.130552
Median Absolute Deviation (MAD)5.2
Skewness1.3926026
Sum328263.8
Variance79.346828
MonotonicityNot monotonic
2025-08-20T15:17:12.869915image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.6 170
 
0.7%
7.7 167
 
0.7%
5.3 163
 
0.7%
6.8 160
 
0.6%
7.8 160
 
0.6%
9 157
 
0.6%
6 157
 
0.6%
7.4 157
 
0.6%
10.7 154
 
0.6%
6.3 154
 
0.6%
Other values (538) 23401
93.6%
ValueCountFrequency (%)
0.1 1
 
< 0.1%
0.2 3
 
< 0.1%
0.3 6
 
< 0.1%
0.4 27
0.1%
0.5 20
0.1%
0.6 29
0.1%
0.7 33
0.1%
0.8 24
0.1%
0.9 38
0.2%
1 32
0.1%
ValueCountFrequency (%)
89.9 1
< 0.1%
80.2 1
< 0.1%
76.9 1
< 0.1%
73.6 1
< 0.1%
72.4 1
< 0.1%
70.6 1
< 0.1%
70 1
< 0.1%
69.6 1
< 0.1%
68.6 1
< 0.1%
67.1 1
< 0.1%

night_shift
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
22438 
1
2562 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters25000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 22438
89.8%
1 2562
 
10.2%

Length

2025-08-20T15:17:12.973548image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-20T15:17:13.126941image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0 22438
89.8%
1 2562
 
10.2%

Most occurring characters

ValueCountFrequency (%)
0 22438
89.8%
1 2562
 
10.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 25000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 22438
89.8%
1 2562
 
10.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 25000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 22438
89.8%
1 2562
 
10.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 25000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 22438
89.8%
1 2562
 
10.2%

schedule_flex
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
15033 
1
9967 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters25000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 15033
60.1%
1 9967
39.9%

Length

2025-08-20T15:17:13.208754image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-20T15:17:13.286249image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0 15033
60.1%
1 9967
39.9%

Most occurring characters

ValueCountFrequency (%)
0 15033
60.1%
1 9967
39.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 25000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 15033
60.1%
1 9967
39.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 25000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 15033
60.1%
1 9967
39.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 25000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 15033
60.1%
1 9967
39.9%

stock_grants
Real number (ℝ)

High correlation  Zeros 

Distinct7648
Distinct (%)30.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12797.86
Minimum0
Maximum209119
Zeros17002
Zeros (%)68.0%
Negative0
Negative (%)0.0%
Memory size195.4 KiB
2025-08-20T15:17:13.385224image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q317546
95-th percentile66207.25
Maximum209119
Range209119
Interquartile range (IQR)17546

Descriptive statistics

Standard deviation24486.708
Coefficient of variation (CV)1.913344
Kurtosis6.9399809
Mean12797.86
Median Absolute Deviation (MAD)0
Skewness2.4137267
Sum3.1994649 × 108
Variance5.9959885 × 108
MonotonicityNot monotonic
2025-08-20T15:17:13.498788image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 17002
68.0%
12139 4
 
< 0.1%
30399 3
 
< 0.1%
62647 3
 
< 0.1%
34413 3
 
< 0.1%
29078 3
 
< 0.1%
20592 3
 
< 0.1%
44592 3
 
< 0.1%
40289 3
 
< 0.1%
26060 3
 
< 0.1%
Other values (7638) 7970
31.9%
ValueCountFrequency (%)
0 17002
68.0%
199 1
 
< 0.1%
311 1
 
< 0.1%
345 1
 
< 0.1%
380 1
 
< 0.1%
386 1
 
< 0.1%
472 1
 
< 0.1%
529 1
 
< 0.1%
558 1
 
< 0.1%
632 1
 
< 0.1%
ValueCountFrequency (%)
209119 1
< 0.1%
206873 1
< 0.1%
206496 1
< 0.1%
204644 1
< 0.1%
193563 1
< 0.1%
193237 1
< 0.1%
192968 1
< 0.1%
192585 1
< 0.1%
192106 1
< 0.1%
187256 1
< 0.1%

benefit_score
Real number (ℝ)

Distinct694
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.0113336
Minimum1.36
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size195.4 KiB
2025-08-20T15:17:13.611046image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1.36
5-th percentile4.92
Q16.14
median7.02
Q37.88
95-th percentile9.15
Maximum10
Range8.64
Interquartile range (IQR)1.74

Descriptive statistics

Standard deviation1.277129
Coefficient of variation (CV)0.18215208
Kurtosis-0.15325863
Mean7.0113336
Median Absolute Deviation (MAD)0.865
Skewness-0.057952014
Sum175283.34
Variance1.6310585
MonotonicityNot monotonic
2025-08-20T15:17:13.730856image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 281
 
1.1%
6.69 98
 
0.4%
6.74 97
 
0.4%
7.55 91
 
0.4%
6.62 91
 
0.4%
6.89 91
 
0.4%
7.56 90
 
0.4%
7.11 87
 
0.3%
6.88 87
 
0.3%
6.7 87
 
0.3%
Other values (684) 23900
95.6%
ValueCountFrequency (%)
1.36 1
 
< 0.1%
2.26 1
 
< 0.1%
2.29 1
 
< 0.1%
2.38 1
 
< 0.1%
2.4 1
 
< 0.1%
2.52 1
 
< 0.1%
2.66 1
 
< 0.1%
2.73 1
 
< 0.1%
2.74 2
< 0.1%
2.76 3
< 0.1%
ValueCountFrequency (%)
10 281
1.1%
9.99 4
 
< 0.1%
9.98 2
 
< 0.1%
9.97 6
 
< 0.1%
9.96 7
 
< 0.1%
9.95 3
 
< 0.1%
9.94 4
 
< 0.1%
9.93 7
 
< 0.1%
9.92 4
 
< 0.1%
9.91 9
 
< 0.1%

sick_days
Real number (ℝ)

Zeros 

Distinct175
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.004832
Minimum0
Maximum20.6
Zeros312
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size195.4 KiB
2025-08-20T15:17:13.847843image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.5
Q14
median5.9
Q37.9
95-th percentile10.9
Maximum20.6
Range20.6
Interquartile range (IQR)3.9

Descriptive statistics

Standard deviation2.8598057
Coefficient of variation (CV)0.47625074
Kurtosis0.076276547
Mean6.004832
Median Absolute Deviation (MAD)1.9
Skewness0.32512578
Sum150120.8
Variance8.1784886
MonotonicityNot monotonic
2025-08-20T15:17:13.964244image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.3 369
 
1.5%
5.4 361
 
1.4%
6.7 359
 
1.4%
6.5 357
 
1.4%
5.2 356
 
1.4%
5.8 355
 
1.4%
5.9 351
 
1.4%
5.5 351
 
1.4%
4.9 350
 
1.4%
4.7 347
 
1.4%
Other values (165) 21444
85.8%
ValueCountFrequency (%)
0 312
1.2%
0.1 32
 
0.1%
0.2 31
 
0.1%
0.3 43
 
0.2%
0.4 54
 
0.2%
0.5 42
 
0.2%
0.6 64
 
0.3%
0.7 58
 
0.2%
0.8 61
 
0.2%
0.9 86
 
0.3%
ValueCountFrequency (%)
20.6 2
< 0.1%
19.6 1
< 0.1%
18.8 1
< 0.1%
18.5 1
< 0.1%
18.4 1
< 0.1%
18.1 1
< 0.1%
18 1
< 0.1%
17.7 1
< 0.1%
17.6 1
< 0.1%
17.5 1
< 0.1%

pto_days_taken
Real number (ℝ)

Distinct324
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.0085
Minimum0
Maximum37
Zeros16
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size195.4 KiB
2025-08-20T15:17:14.075744image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7.7
Q112.7
median16
Q319.3
95-th percentile24.2
Maximum37
Range37
Interquartile range (IQR)6.6

Descriptive statistics

Standard deviation4.9852799
Coefficient of variation (CV)0.31141456
Kurtosis-0.0052182996
Mean16.0085
Median Absolute Deviation (MAD)3.3
Skewness0.0018824927
Sum400212.5
Variance24.853016
MonotonicityNot monotonic
2025-08-20T15:17:14.198871image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16.4 233
 
0.9%
15.4 224
 
0.9%
15.8 224
 
0.9%
16.7 222
 
0.9%
16.9 217
 
0.9%
16.3 213
 
0.9%
16.6 213
 
0.9%
17.2 209
 
0.8%
17.1 208
 
0.8%
15.2 206
 
0.8%
Other values (314) 22831
91.3%
ValueCountFrequency (%)
0 16
0.1%
0.1 1
 
< 0.1%
0.2 1
 
< 0.1%
0.4 2
 
< 0.1%
0.5 1
 
< 0.1%
0.7 4
 
< 0.1%
0.8 2
 
< 0.1%
1 3
 
< 0.1%
1.1 3
 
< 0.1%
1.2 5
 
< 0.1%
ValueCountFrequency (%)
37 1
< 0.1%
34.4 1
< 0.1%
34.1 2
< 0.1%
33.9 1
< 0.1%
33.4 1
< 0.1%
33.2 1
< 0.1%
33.1 1
< 0.1%
32.9 2
< 0.1%
32.7 2
< 0.1%
32.4 1
< 0.1%

leave_last_yr
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
21294 
1
3706 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters25000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 21294
85.2%
1 3706
 
14.8%

Length

2025-08-20T15:17:14.308285image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-20T15:17:14.384540image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0 21294
85.2%
1 3706
 
14.8%

Most occurring characters

ValueCountFrequency (%)
0 21294
85.2%
1 3706
 
14.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 25000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 21294
85.2%
1 3706
 
14.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 25000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 21294
85.2%
1 3706
 
14.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 25000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 21294
85.2%
1 3706
 
14.8%

team_id
Real number (ℝ)

Distinct400
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean200.80212
Minimum1
Maximum400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size195.4 KiB
2025-08-20T15:17:14.482393image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile20
Q1101
median201
Q3300
95-th percentile380
Maximum400
Range399
Interquartile range (IQR)199

Descriptive statistics

Standard deviation115.31722
Coefficient of variation (CV)0.57428286
Kurtosis-1.1970687
Mean200.80212
Median Absolute Deviation (MAD)99
Skewness-0.0069062091
Sum5020053
Variance13298.06
MonotonicityNot monotonic
2025-08-20T15:17:14.609296image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
353 90
 
0.4%
48 82
 
0.3%
175 80
 
0.3%
223 79
 
0.3%
116 79
 
0.3%
259 79
 
0.3%
272 78
 
0.3%
64 78
 
0.3%
242 78
 
0.3%
5 77
 
0.3%
Other values (390) 24200
96.8%
ValueCountFrequency (%)
1 58
0.2%
2 70
0.3%
3 65
0.3%
4 61
0.2%
5 77
0.3%
6 68
0.3%
7 61
0.2%
8 74
0.3%
9 61
0.2%
10 54
0.2%
ValueCountFrequency (%)
400 73
0.3%
399 51
0.2%
398 64
0.3%
397 74
0.3%
396 73
0.3%
395 58
0.2%
394 75
0.3%
393 63
0.3%
392 61
0.2%
391 68
0.3%

exit_interview_scheduled
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
21477 
1
3523 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters25000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 21477
85.9%
1 3523
 
14.1%

Length

2025-08-20T15:17:14.719231image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-20T15:17:14.798098image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0 21477
85.9%
1 3523
 
14.1%

Most occurring characters

ValueCountFrequency (%)
0 21477
85.9%
1 3523
 
14.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 25000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 21477
85.9%
1 3523
 
14.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 25000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 21477
85.9%
1 3523
 
14.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 25000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 21477
85.9%
1 3523
 
14.1%

offboarding_ticket_created
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
21898 
1
3102 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters25000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 21898
87.6%
1 3102
 
12.4%

Length

2025-08-20T15:17:14.882510image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-20T15:17:14.964344image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0 21898
87.6%
1 3102
 
12.4%

Most occurring characters

ValueCountFrequency (%)
0 21898
87.6%
1 3102
 
12.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 25000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 21898
87.6%
1 3102
 
12.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 25000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 21898
87.6%
1 3102
 
12.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 25000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 21898
87.6%
1 3102
 
12.4%

attrited
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
20852 
1
4148 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters25000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 20852
83.4%
1 4148
 
16.6%

Length

2025-08-20T15:17:15.050124image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-20T15:17:15.126367image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0 20852
83.4%
1 4148
 
16.6%

Most occurring characters

ValueCountFrequency (%)
0 20852
83.4%
1 4148
 
16.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 25000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 20852
83.4%
1 4148
 
16.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 25000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 20852
83.4%
1 4148
 
16.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 25000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 20852
83.4%
1 4148
 
16.6%

months_since_hire
Real number (ℝ)

High correlation 

Distinct1742
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean89.988936
Minimum0.8
Maximum179.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size195.4 KiB
2025-08-20T15:17:15.293957image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0.8
5-th percentile26.9
Q159.9
median90.3
Q3120
95-th percentile152.3
Maximum179.3
Range178.5
Interquartile range (IQR)60.1

Descriptive statistics

Standard deviation38.69745
Coefficient of variation (CV)0.43002453
Kurtosis-0.82547151
Mean89.988936
Median Absolute Deviation (MAD)30.1
Skewness-0.010317304
Sum2249723.4
Variance1497.4926
MonotonicityNot monotonic
2025-08-20T15:17:15.410140image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
71.6 36
 
0.1%
64.8 36
 
0.1%
101.1 34
 
0.1%
100.7 34
 
0.1%
94.1 33
 
0.1%
104 32
 
0.1%
60.3 32
 
0.1%
69 32
 
0.1%
87.5 31
 
0.1%
54.4 31
 
0.1%
Other values (1732) 24669
98.7%
ValueCountFrequency (%)
0.8 1
< 0.1%
1.1 2
< 0.1%
1.2 1
< 0.1%
1.5 2
< 0.1%
1.7 1
< 0.1%
1.8 1
< 0.1%
1.9 2
< 0.1%
2.3 1
< 0.1%
2.5 2
< 0.1%
2.7 1
< 0.1%
ValueCountFrequency (%)
179.3 1
< 0.1%
179.2 1
< 0.1%
179 1
< 0.1%
178.6 2
< 0.1%
178.3 1
< 0.1%
178 1
< 0.1%
177.9 1
< 0.1%
177.6 1
< 0.1%
177.5 2
< 0.1%
177.4 1
< 0.1%

salary_band
Categorical

High correlation  Uniform 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
Q1
5001 
Q4
5000 
Q5
5000 
Q3
5000 
Q2
4999 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters50000
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowQ4
2nd rowQ5
3rd rowQ3
4th rowQ4
5th rowQ3

Common Values

ValueCountFrequency (%)
Q1 5001
20.0%
Q4 5000
20.0%
Q5 5000
20.0%
Q3 5000
20.0%
Q2 4999
20.0%

Length

2025-08-20T15:17:15.516516image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-20T15:17:15.601707image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
q1 5001
20.0%
q4 5000
20.0%
q5 5000
20.0%
q3 5000
20.0%
q2 4999
20.0%

Most occurring characters

ValueCountFrequency (%)
Q 25000
50.0%
1 5001
 
10.0%
4 5000
 
10.0%
5 5000
 
10.0%
3 5000
 
10.0%
2 4999
 
10.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 50000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
Q 25000
50.0%
1 5001
 
10.0%
4 5000
 
10.0%
5 5000
 
10.0%
3 5000
 
10.0%
2 4999
 
10.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 50000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
Q 25000
50.0%
1 5001
 
10.0%
4 5000
 
10.0%
5 5000
 
10.0%
3 5000
 
10.0%
2 4999
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 50000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
Q 25000
50.0%
1 5001
 
10.0%
4 5000
 
10.0%
5 5000
 
10.0%
3 5000
 
10.0%
2 4999
 
10.0%

Interactions

2025-08-20T15:17:05.045081image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-20T15:16:33.419515image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-20T15:16:35.059513image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-20T15:16:36.698358image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-20T15:16:38.347348image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-20T15:16:40.070717image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-20T15:16:41.737779image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-20T15:16:43.383262image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-20T15:16:44.973225image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-20T15:16:46.701764image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-20T15:16:48.318137image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-20T15:16:50.092763image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-20T15:16:51.674528image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-20T15:16:53.405887image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-20T15:16:55.045417image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-20T15:16:56.709641image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-20T15:16:58.328136image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-20T15:17:00.014452image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-20T15:17:01.595674image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-20T15:17:03.338120image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-20T15:17:05.126174image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-20T15:16:33.500140image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-20T15:16:35.137230image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-20T15:16:36.779352image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-20T15:16:38.430489image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-20T15:16:40.152187image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-20T15:16:41.814698image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-20T15:16:43.460666image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-20T15:16:45.054493image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-20T15:16:46.781316image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-20T15:16:48.405562image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-20T15:16:50.173762image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-20T15:16:51.751876image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-20T15:16:53.489505image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-20T15:16:55.122486image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-20T15:16:56.788510image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-20T15:16:58.405387image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-20T15:17:00.091450image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-20T15:17:01.679189image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-20T15:17:03.426387image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-20T15:17:05.206295image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-20T15:16:33.576252image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-20T15:16:35.211109image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-20T15:16:36.857981image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-20T15:16:38.509175image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-20T15:16:40.231384image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-20T15:16:41.892447image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-20T15:16:43.534591image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-20T15:16:45.131944image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-20T15:16:46.856090image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-20T15:16:48.488060image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-20T15:16:50.247453image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-20T15:16:51.827811image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-20T15:16:53.567715image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-20T15:16:55.196972image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-20T15:16:56.866223image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-20T15:16:58.480333image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-20T15:17:00.167000image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-20T15:17:01.760729image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-20T15:17:03.507212image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-20T15:17:05.292925image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-20T15:16:33.661699image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-20T15:16:35.291343image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-20T15:16:36.942595image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-20T15:16:38.595264image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-20T15:16:40.319383image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-20T15:16:41.973719image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-20T15:16:43.617001image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-20T15:16:45.213603image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-20T15:16:46.938540image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-20T15:16:48.575547image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-20T15:16:50.326989image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-20T15:16:51.913912image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-20T15:16:53.651886image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-20T15:16:55.280791image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-20T15:16:56.949956image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-20T15:16:58.573143image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-20T15:17:00.247702image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-20T15:17:01.849796image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-20T15:17:03.592841image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-20T15:17:05.380075image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-20T15:16:33.748019image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-20T15:16:35.373970image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-20T15:16:37.029271image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-20T15:16:38.682644image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-20T15:16:40.411933image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-20T15:16:42.058485image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-20T15:16:43.701631image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-20T15:16:45.298101image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-20T15:16:47.021684image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-20T15:16:48.664967image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-20T15:16:50.410527image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-20T15:16:52.028629image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
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2025-08-20T15:16:56.223073image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
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2025-08-20T15:17:06.387494image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
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2025-08-20T15:16:41.313197image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
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2025-08-20T15:16:44.567245image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
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2025-08-20T15:16:41.471956image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
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2025-08-20T15:16:44.717080image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-20T15:16:46.359109image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-20T15:16:48.054275image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-20T15:16:49.821464image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
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2025-08-20T15:17:01.508113image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-20T15:17:03.249902image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-20T15:17:04.953734image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Correlations

2025-08-20T15:17:15.702498image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ageattritedavg_raise_3ybase_salarybenefit_scorecommute_kmcompa_ratiodepartmentemployee_idengagement_scoreexit_interview_scheduledgenderinternal_moves_last_2yis_managerlearning_hours_last_yrleave_last_yrlevelmanager_qualitymonths_since_hirenight_shiftoffboarding_ticket_createdovertime_hours_monthperformance_ratingpto_days_takenregionremote_statusrolesalary_bandschedule_flexsick_daysstock_grantsteam_idtenure_yearstime_since_last_promo_yrsworkload_score
age1.0000.0160.0060.0010.016-0.003-0.0040.0070.005-0.0080.0140.0130.0040.000-0.0160.0000.0000.0050.0030.0150.014-0.0090.000-0.0110.0060.0000.0000.0000.0000.0140.000-0.0060.003-0.0020.005
attrited0.0161.0000.0000.0430.0410.0580.0400.0240.0000.1040.9080.0000.0280.0250.0690.0090.0310.1020.0480.0060.8440.0000.1110.0000.0070.0560.0260.0470.0330.0000.0390.0180.0490.0650.043
avg_raise_3y0.0060.0001.0000.0040.0140.0120.0150.0060.008-0.0070.0000.0000.0060.0150.0040.0000.000-0.006-0.0050.0000.007-0.0130.001-0.0110.0060.0090.0160.0000.0160.0000.008-0.004-0.0050.014-0.003
base_salary0.0010.0430.0041.000-0.003-0.0070.3830.1810.004-0.0020.0390.0000.0020.6110.0010.0000.4440.0040.0030.0130.0340.0040.000-0.0060.0000.0000.1600.6900.004-0.0050.6200.0090.003-0.0060.001
benefit_score0.0160.0410.014-0.0031.0000.0020.0010.0140.0060.0080.0400.000-0.0030.0000.0000.0000.004-0.000-0.0060.0000.037-0.0010.0090.0060.0130.0100.0160.0080.000-0.0020.0130.000-0.0060.006-0.000
commute_km-0.0030.0580.012-0.0070.0021.000-0.0040.0000.007-0.0050.0530.017-0.0050.000-0.0050.0110.004-0.0120.0010.0080.048-0.0080.000-0.0030.0000.4500.0000.0000.0000.000-0.0050.0060.0010.003-0.003
compa_ratio-0.0040.0400.0150.3830.001-0.0041.0000.0000.0030.0050.0360.0000.0020.000-0.0060.0000.000-0.0020.0030.0000.029-0.0030.0000.0040.0000.0000.0000.1900.013-0.003-0.0020.0030.003-0.0080.005
department0.0070.0240.0060.1810.0140.0000.0001.0000.0000.0000.0170.0110.0100.0070.0070.0090.0000.0040.0030.0000.0230.0000.0110.0000.0150.0000.9990.2400.0000.0000.0000.0030.0020.0090.007
employee_id0.0050.0000.0080.0040.0060.0070.0030.0001.000-0.0080.0000.0000.0000.008-0.0050.0000.0000.001-0.0070.0130.0000.0060.0040.0010.0000.0000.0080.0080.010-0.008-0.003-0.004-0.007-0.0120.005
engagement_score-0.0080.104-0.007-0.0020.008-0.0050.0050.000-0.0081.0000.0960.006-0.0020.0060.0050.0000.000-0.0000.0050.0110.087-0.0060.006-0.0050.0000.0000.0000.0000.0000.014-0.0080.0020.0050.0010.015
exit_interview_scheduled0.0140.9080.0000.0390.0400.0530.0360.0170.0000.0961.0000.0000.0240.0270.0640.0060.0310.0910.0420.0020.7710.0000.1010.0000.0080.0490.0240.0420.0270.0000.0370.0150.0430.0600.041
gender0.0130.0000.0000.0000.0000.0170.0000.0110.0000.0060.0001.0000.0000.0000.0000.0000.0000.0000.0140.0000.0000.0000.0040.0000.0060.0000.0180.0000.0000.0050.0000.0110.0140.0000.010
internal_moves_last_2y0.0040.0280.0060.002-0.003-0.0050.0020.0100.000-0.0020.0240.0001.0000.014-0.0020.0000.018-0.004-0.0000.0000.021-0.0030.000-0.0070.0000.0000.0040.0000.000-0.014-0.004-0.000-0.0000.005-0.000
is_manager0.0000.0250.0150.6110.0000.0000.0000.0070.0080.0060.0270.0000.0141.0000.0000.0001.0000.0100.0130.0000.0210.0190.0000.0170.0070.0000.0250.5750.0000.0170.5890.0000.0160.0000.000
learning_hours_last_yr-0.0160.0690.0040.0010.000-0.005-0.0060.007-0.0050.0050.0640.000-0.0020.0001.0000.0110.0000.0070.0060.0000.062-0.0040.000-0.0040.0090.0160.0000.0000.000-0.0020.000-0.0040.0060.0000.003
leave_last_yr0.0000.0090.0000.0000.0000.0110.0000.0090.0000.0000.0060.0000.0000.0000.0111.0000.0000.0000.0000.0000.0120.0070.0000.0000.0000.0000.0070.0060.0000.0200.0000.0120.0000.0000.000
level0.0000.0310.0000.4440.0040.0040.0000.0000.0000.0000.0310.0000.0181.0000.0000.0001.0000.0070.0130.0120.0260.0040.0000.0070.0000.0080.0100.4350.0000.0110.3260.0000.0140.0000.006
manager_quality0.0050.102-0.0060.004-0.000-0.012-0.0020.0040.001-0.0000.0910.000-0.0040.0100.0070.0000.0071.0000.0020.0120.0860.0090.000-0.0010.0030.0060.0000.0110.0130.0030.008-0.0000.0020.0030.005
months_since_hire0.0030.048-0.0050.003-0.0060.0010.0030.003-0.0070.0050.0420.014-0.0000.0130.0060.0000.0130.0021.0000.0000.039-0.0030.000-0.0050.0000.0010.0000.0090.0000.0030.001-0.0001.0000.005-0.012
night_shift0.0150.0060.0000.0130.0000.0080.0000.0000.0130.0110.0020.0000.0000.0000.0000.0000.0120.0120.0001.0000.0090.0000.0000.0130.0050.0040.0020.0000.0000.0050.0000.0110.0000.0110.013
offboarding_ticket_created0.0140.8440.0070.0340.0370.0480.0290.0230.0000.0870.7710.0000.0210.0210.0620.0120.0260.0860.0390.0091.0000.0000.0930.0000.0120.0460.0270.0370.0310.0000.0340.0000.0400.0620.034
overtime_hours_month-0.0090.000-0.0130.004-0.001-0.008-0.0030.0000.006-0.0060.0000.000-0.0030.019-0.0040.0070.0040.009-0.0030.0000.0001.0000.011-0.0050.0000.0000.0000.0090.0000.000-0.0060.002-0.003-0.002-0.002
performance_rating0.0000.1110.0010.0000.0090.0000.0000.0110.0040.0060.1010.0040.0000.0000.0000.0000.0000.0000.0000.0000.0930.0111.0000.0000.0000.0000.0130.0090.0080.0000.0000.0000.0000.0050.002
pto_days_taken-0.0110.000-0.011-0.0060.006-0.0030.0040.0000.001-0.0050.0000.000-0.0070.017-0.0040.0000.007-0.001-0.0050.0130.000-0.0050.0001.0000.0000.0090.0000.0000.0070.0050.001-0.008-0.005-0.0090.008
region0.0060.0070.0060.0000.0130.0000.0000.0150.0000.0000.0080.0060.0000.0070.0090.0000.0000.0030.0000.0050.0120.0000.0000.0001.0000.0000.0200.0000.0000.0100.0040.0000.0000.0000.016
remote_status0.0000.0560.0090.0000.0100.4500.0000.0000.0000.0000.0490.0000.0000.0000.0160.0000.0080.0060.0010.0040.0460.0000.0000.0090.0001.0000.0060.0020.0000.0000.0030.0070.0000.0000.000
role0.0000.0260.0160.1600.0160.0000.0000.9990.0080.0000.0240.0180.0040.0250.0000.0070.0100.0000.0000.0020.0270.0000.0130.0000.0200.0061.0000.2410.0000.0000.0000.0070.0000.0000.011
salary_band0.0000.0470.0000.6900.0080.0000.1900.2400.0080.0000.0420.0000.0000.5750.0000.0060.4350.0110.0090.0000.0370.0090.0090.0000.0000.0020.2411.0000.0000.0000.2570.0000.0090.0000.000
schedule_flex0.0000.0330.0160.0040.0000.0000.0130.0000.0100.0000.0270.0000.0000.0000.0000.0000.0000.0130.0000.0000.0310.0000.0080.0070.0000.0000.0000.0001.0000.0000.0000.0000.0000.0120.000
sick_days0.0140.0000.000-0.005-0.0020.000-0.0030.000-0.0080.0140.0000.005-0.0140.017-0.0020.0200.0110.0030.0030.0050.0000.0000.0000.0050.0100.0000.0000.0000.0001.0000.0020.0050.003-0.0020.015
stock_grants0.0000.0390.0080.6200.013-0.005-0.0020.000-0.003-0.0080.0370.000-0.0040.5890.0000.0000.3260.0080.0010.0000.034-0.0060.0000.0010.0040.0030.0000.2570.0000.0021.0000.0100.001-0.0010.004
team_id-0.0060.018-0.0040.0090.0000.0060.0030.003-0.0040.0020.0150.011-0.0000.000-0.0040.0120.000-0.000-0.0000.0110.0000.0020.000-0.0080.0000.0070.0070.0000.0000.0050.0101.000-0.000-0.002-0.007
tenure_years0.0030.049-0.0050.003-0.0060.0010.0030.002-0.0070.0050.0430.014-0.0000.0160.0060.0000.0140.0021.0000.0000.040-0.0030.000-0.0050.0000.0000.0000.0090.0000.0030.001-0.0001.0000.005-0.012
time_since_last_promo_yrs-0.0020.0650.014-0.0060.0060.003-0.0080.009-0.0120.0010.0600.0000.0050.0000.0000.0000.0000.0030.0050.0110.062-0.0020.005-0.0090.0000.0000.0000.0000.012-0.002-0.001-0.0020.0051.000-0.002
workload_score0.0050.043-0.0030.001-0.000-0.0030.0050.0070.0050.0150.0410.010-0.0000.0000.0030.0000.0060.005-0.0120.0130.034-0.0020.0020.0080.0160.0000.0110.0000.0000.0150.004-0.007-0.012-0.0021.000

Missing values

2025-08-20T15:17:06.894504image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
A simple visualization of nullity by column.
2025-08-20T15:17:07.303584image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-08-20T15:17:07.578777image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

employee_idsnapshot_datehire_dateregiondepartmentrolelevelis_manageragegenderremote_statuscommute_kmtenure_yearsbase_salarycompa_ratioavg_raise_3ytime_since_last_promo_yrsinternal_moves_last_2yperformance_ratingengagement_scoremanager_qualityworkload_scorelearning_hours_last_yrovertime_hours_monthnight_shiftschedule_flexstock_grantsbenefit_scoresick_dayspto_days_takenleave_last_yrteam_idexit_interview_scheduledoffboarding_ticket_createdattritedmonths_since_hiresalary_band
01217522020-05-132014-08-10AMERSalesSales ManagerIC3033FHybrid20.45.76135037.01.230.0340.71047.026.735.727.03.2000.06.999.116.708500069.1Q4
11072082020-09-202011-07-14APACEngineeringSWE IIManager131FHybrid13.89.19166104.00.950.0271.43039.527.336.1542.817.30073464.07.758.415.616000110.2Q5
21170582020-12-292013-07-28EMEASalesAEIC2040MOnsite20.57.42113486.01.210.0284.67147.457.936.1834.913.5000.06.766.619.5017700089.1Q3
31207542023-10-312012-04-06AMEREngineeringML EngineerIC4026MRemote0.011.57128470.00.800.0340.8305NaN6.338.2611.18.10033159.07.849.418.80232000138.8Q4
41225052022-07-202015-05-25APACHRHRBPIC3031FOnsite29.37.15106256.01.060.0331.15037.466.264.8414.013.1000.06.055.417.4032810185.8Q3
51100572024-01-142017-12-24APACOperationsOps AnalystVP140FRemote0.06.06220939.01.100.0251.6023NaN10.004.9140.73.60041217.07.909.219.5018400072.7Q5
61013512024-07-102015-05-31AMEREngineeringSWE IIC3022FHybrid32.89.11143768.01.030.0292.57034.684.746.6626.19.4000.08.085.519.5018111109.3Q5
71069402023-11-252012-02-14APACFinanceAccountantIC2025FOnsite9.511.7890998.00.970.0361.02136.616.087.4461.810.4110.06.873.015.20395000141.3Q2
81181862021-10-142010-01-20AMERSalesAEIC1028MRemote0.011.7368614.00.890.0301.62049.488.665.2841.516.3010.06.842.417.81169000140.8Q1
91194892022-08-062013-09-02APACMarketingBrandIC3046MHybrid3.68.9397654.00.930.0270.73157.285.498.2016.911.8000.08.264.717.20289000107.1Q2
employee_idsnapshot_datehire_dateregiondepartmentrolelevelis_manageragegenderremote_statuscommute_kmtenure_yearsbase_salarycompa_ratioavg_raise_3ytime_since_last_promo_yrsinternal_moves_last_2yperformance_ratingengagement_scoremanager_qualityworkload_scorelearning_hours_last_yrovertime_hours_monthnight_shiftschedule_flexstock_grantsbenefit_scoresick_dayspto_days_takenleave_last_yrteam_idexit_interview_scheduledoffboarding_ticket_createdattritedmonths_since_hiresalary_band
249901208252023-01-142015-10-12EMEAMarketingBrandManager145FHybrid21.37.26142382.01.080.0320.81128.997.608.2111.229.10025294.06.526.719.7028800087.1Q4
249911025832022-07-062015-07-11APACProductUX ResearcherIC3034FOnsite3.36.99129090.00.960.0341.54136.97NaN4.108.316.7000.07.746.48.4034600083.8Q4
249921239502022-04-202019-11-24EMEAEngineeringML EngineerIC2035MHybrid13.72.40114436.00.960.0371.721310.005.777.7629.315.7000.05.709.219.7011811128.8Q3
249931013722023-06-142016-03-18AMERSalesSDRIC1038FRemote0.07.2485126.01.110.0200.73037.768.237.061.81.8000.07.236.819.408310186.9Q2
249941139272024-07-042015-07-02AMERProductUX ResearcherIC4032FHybrid2.39.01121153.00.780.0231.31058.264.797.470.611.30026991.08.750.618.4051000108.1Q4
249951009192023-10-232018-08-24APACOperationsOps AnalystIC3018MRemote0.05.16110413.01.100.0263.25038.148.556.157.313.4010.08.460.627.9112900062.0Q3
249961206912020-05-212019-08-25APACSalesAEIC1047MOnsite39.50.7479520.01.030.0291.46035.815.897.663.714.4000.06.223.117.40420008.9Q1
249971056992020-09-182017-08-28APACOperationsOps AnalystIC1050FHybrid43.53.0684228.01.200.0443.380310.004.622.7159.18.9000.06.328.17.1018700036.7Q1
249981107422022-12-042012-11-10APACEngineeringSWE IIManager138MHybrid8.510.06155079.00.890.0200.49036.5610.007.8018.715.4006397.06.807.619.80128000120.8Q5
249991169212023-07-252016-04-24APACFinanceFinance ManagerManager135MRemote0.07.25140194.01.020.0362.66246.895.347.849.212.00042331.07.9611.418.1011500087.0Q4